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Abstract
In this paper, hybrid genetic-SPSA algorithm based on random fuzzy simulation is proposed for solving chance-constrained programming in random fuzzy decision-making systems by combining random fuzzy simulation, genetic algorithm (GA), and simultaneous perturbation stochastic approximation (SPSA). In the provided algorithm, random fuzzy simulation is designed to estimate the chance of a random fuzzy event and the optimistic value to a random fuzzy variable, GA is employed to search for the optimal solution in the entire space, and SPSA is used to improve the new chromosomes obtained by crossover and mutation operations at each generation in GA. At the end of this paper, an example is given to illustrate the effectiveness of the presented algorithm.
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Authors and Affiliations
Institute of Systems Engineering, Tianjin University, Tianjin, 300072, China
Yufu Ning & Wansheng Tang
Department of Computer Science, Dezhou University, Dezhou, 253023, China
Yufu Ning
Department of Statistics, Henan Institute of Finance and Economics, Zhengzhou, 450002, China
Hui Wang
- Yufu Ning
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- Wansheng Tang
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- Hui Wang
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Editors and Affiliations
School of Electrical and Electronic Engineering, Nanyang Technological University, Block S1, Nanyang Avenue, 639798, Singapore
Lipo Wang
Honda Research Institute Europe GmbH, Offenbach/Main, Germany
Yaochu Jin
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© 2005 Springer-Verlag Berlin Heidelberg
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Ning, Y., Tang, W., Wang, H. (2005). Hybrid Genetic-SPSA Algorithm Based on Random Fuzzy Simulation for Chance-Constrained Programming. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_41
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